CMIN - a CRISP-DM-based case tool for supporting data mining projects

Abstract

This paper introduces CMIN, an integrated computer aided software engineering (CASE) tool based on cross-industry standard process for data mining (CRISP-DM) 1.0 designed to support carrying out data mining projects. It is “integrated” in the sense that it supports all phases of a process. A general overview of how CMIN works is presented first, including a treatment of processes, templates and project management. CMIN´s capacity for easily and intuitively monitoring projects is highlighted, as is the manner in which CMIN allows a user to increase knowledge regarding using CRISP-DM or any other process defined in the CASE tool through the help and information presented in each step. Next, it is shown how CMIN can bind new data mining algorithms in runtime (without the need to recompile the tool) to support modelling tasks (based on a Workflow) and evaluate data mining projects. Finally, the results of two evaluations of the tool, some conclusions and suggestions for future work are presented.

A New Methodological Framework for Project Design to Analyse and Prevent Students from Dropping Out of Higher Education

“…This fusion poses a new methodological analysis for a technological project that seeks to address a negative situation in society. Considering that the CRISP-DM methodology focuses on the business objectives of the project, it becomes necessary to reinforce its six phases [44][45][46]50] with the first stage of the LFM, with the objective of focusing the problem and deploying actions aligned to the solution alternatives identified for a technological project. In this study, a framework is proposed for the process of identifying the problem of student dropout in a public university and proposing solution alternatives based on Machine Learning techniques.…”

“…A technology project under this approach provides a powerful tool for education administrators to make the best decisions for student retention based on data [29,30,40,43,44]. One of the most widely used management methodologies for these projects is the CRIPS-DM methodology, a tool that covers all the stages for the implementation of a technology project using Data Mining (DM) [43][44][45][46][47][48], focusing its analysis on the technological part.…”

“…CRISP-DM (Cross Industry Standard Process for Data Mining) and SEMMA (Sample, Explore, Modify, Model, Assess) are the most prominent methodologies for DM projects. SEMMA focuses on the technical characteristics of the process development [46,49] and CRISP-DM focuses on the business objectives of the project [43][44][45][46]49,50]. Another advantage of CRISP-DM over SEMMA relies on its ability to be integrated with a project management methodology that complements the administrative and technical tasks [46], a recommendation that is precisely developed in the present work where CRISP-DM and the LFM will be integrated.…”